{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdp</th>\n",
       "      <th>gdp_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992Q1</th>\n",
       "      <td>5262.8</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992Q2</th>\n",
       "      <td>6484.3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992Q3</th>\n",
       "      <td>7192.6</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992Q4</th>\n",
       "      <td>8254.8</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q1</th>\n",
       "      <td>6834.6</td>\n",
       "      <td>0.298662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gdp  gdp_rate\n",
       "date                    \n",
       "1992Q1  5262.8  0.250000\n",
       "1992Q2  6484.3  0.250000\n",
       "1992Q3  7192.6  0.250000\n",
       "1992Q4  8254.8  0.250000\n",
       "1993Q1  6834.6  0.298662"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ===GDP===\n",
    "# read data\n",
    "os.chdir('/home/shensir/Documents/MyPrograming/Python/PycharmProject/MySpider/Stock/src')\n",
    "gdp_df = pd.read_csv('../data/gdp_q4_processed.csv')\n",
    "gdp_df.index = gdp_df['date']\n",
    "gdp_df.drop(['date'], inplace=True, axis=1)\n",
    "gdp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>close</th>\n",
       "      <th>trade_times</th>\n",
       "      <th>trade_money</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992Q1</th>\n",
       "      <td>1992.0</td>\n",
       "      <td>339.611967</td>\n",
       "      <td>4.698902e+05</td>\n",
       "      <td>8.773705e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992Q2</th>\n",
       "      <td>1992.0</td>\n",
       "      <td>778.056406</td>\n",
       "      <td>3.715642e+06</td>\n",
       "      <td>7.651284e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992Q3</th>\n",
       "      <td>1992.0</td>\n",
       "      <td>928.480909</td>\n",
       "      <td>8.362256e+06</td>\n",
       "      <td>1.269380e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992Q4</th>\n",
       "      <td>1992.0</td>\n",
       "      <td>604.371875</td>\n",
       "      <td>1.571860e+07</td>\n",
       "      <td>1.770287e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993Q1</th>\n",
       "      <td>1993.0</td>\n",
       "      <td>1182.115574</td>\n",
       "      <td>4.465464e+07</td>\n",
       "      <td>8.546447e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year        close   trade_times   trade_money\n",
       "date                                                   \n",
       "1992Q1  1992.0   339.611967  4.698902e+05  8.773705e+06\n",
       "1992Q2  1992.0   778.056406  3.715642e+06  7.651284e+07\n",
       "1992Q3  1992.0   928.480909  8.362256e+06  1.269380e+08\n",
       "1992Q4  1992.0   604.371875  1.571860e+07  1.770287e+08\n",
       "1993Q1  1993.0  1182.115574  4.465464e+07  8.546447e+08"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ===Stock===\n",
    "stock_df = pd.read_csv('../data/stock_processed.csv')\n",
    "stock_df.index = stock_df['date']\n",
    "stock_df.drop('date',inplace=True, axis=1)\n",
    "stock_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff400104978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "# plt.subplot(121)\n",
    "gdp_df['gdp_rate'].plot()\n",
    "plt.xticks(rotation=60)\n",
    "# plt.subplot(122)\n",
    "# stock_df['close'].plot()\n",
    "# plt.xticks(rotation=60)\n",
    "# plt.subplots_adjust(left=0.2, wspace=0.3, top=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdp</th>\n",
       "      <th>gdp_rate</th>\n",
       "      <th>year</th>\n",
       "      <th>close</th>\n",
       "      <th>trade_times</th>\n",
       "      <th>trade_money</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998Q1</th>\n",
       "      <td>18049.1</td>\n",
       "      <td>0.081490</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1212.777731</td>\n",
       "      <td>4.150183e+08</td>\n",
       "      <td>4.453512e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998Q2</th>\n",
       "      <td>20296.6</td>\n",
       "      <td>0.059123</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1353.313203</td>\n",
       "      <td>8.650186e+08</td>\n",
       "      <td>7.784021e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998Q3</th>\n",
       "      <td>21775.6</td>\n",
       "      <td>0.062178</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1242.377197</td>\n",
       "      <td>5.720185e+08</td>\n",
       "      <td>4.449456e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998Q4</th>\n",
       "      <td>25074.2</td>\n",
       "      <td>0.073313</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1227.214984</td>\n",
       "      <td>4.852628e+08</td>\n",
       "      <td>4.308763e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999Q1</th>\n",
       "      <td>19361.9</td>\n",
       "      <td>0.072735</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>1141.768320</td>\n",
       "      <td>5.138045e+08</td>\n",
       "      <td>3.505024e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gdp  gdp_rate    year        close   trade_times   trade_money\n",
       "date                                                                      \n",
       "1998Q1  18049.1  0.081490  1998.0  1212.777731  4.150183e+08  4.453512e+09\n",
       "1998Q2  20296.6  0.059123  1998.0  1353.313203  8.650186e+08  7.784021e+09\n",
       "1998Q3  21775.6  0.062178  1998.0  1242.377197  5.720185e+08  4.449456e+09\n",
       "1998Q4  25074.2  0.073313  1998.0  1227.214984  4.852628e+08  4.308763e+09\n",
       "1999Q1  19361.9  0.072735  1999.0  1141.768320  5.138045e+08  3.505024e+09"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 选取1998-2014\n",
    "data = pd.concat([gdp_df.loc['1998Q1':'2014Q4'], stock_df.loc['1998Q1':'2014Q4']], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3fc81c1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd_data = pd.DataFrame()\n",
    "sd_data['gdp_rate_sd'] = (data['gdp_rate']-data['gdp_rate'].min())/(data['gdp_rate'].max()-data['gdp_rate'].min())\n",
    "sd_data['stock_close_sd'] = (data['close']-data['close'].min())/(data['close'].max()-data['close'].min())\n",
    "sd_data.plot()\n",
    "plt.xticks(rotation=60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('../data/stock_gdp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
